Time domain filter comparison in passive radar systems

Pető, Tamás; Seller, Rudolf

doi:10.23919/irs.2017.8008108

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…The column space of is constructed of delayed replicas of reference signal. Also, the clutter signal in (4) is an element-wise product of a delayed replica of and a weight vector in form (11). Therefore, the elementwise product of the i-th column of and represents a clutter signal of the form 4…”

Section: The Weight Matrix Representationmentioning

confidence: 99%

“…Numerical optimization of the Wiener-Hopf equations are found to achieve near-optimal performance with a fraction of the computational cost. Comparing the LMS, RLS, NLMS, ECA, and ECA-B, [11] declares that the first three algorithms have their worst detection performance if targets are located in the same interval of clutters range. Also, the ECA-B algorithm is introduced as a high-performance method with less computational complexity than ECA.…”

Section: Introductionmentioning

confidence: 99%

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Weight Matrix-Based Representation of Sub-Optimum Disturbance Cancellation Filters

VenuDunde¹,

Rao²

2019

IJISA

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The disturbance cancellation techniques are investigated in this paper for Passive Bistatic Radars. The conventional procedure is to compute a clean signal by iteratively constructing an error vector from the residual of the surveillance samples after subtraction of a linear combination of clutters samples. A weight vector is eventually extracted in pure block algorithms, while a weight matrix is computed in iterative schemes. It is illustrated in this paper that the computed weight matrix in the latter case contains valuable information describing the clutters properties. The weight matrix-based disturbance attenuation technique is then innovated and its effectiveness is compared to the conventional errorbased procedure in the test bed of several available iterative algorithms. Moreover, a revision of the FBLMS algorithm is presented to cover the case of complex input signals.

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Section: The Weight Matrix Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weight Matrix-Based Representation of Sub-Optimum Disturbance Cancellation Filters

VenuDunde¹,

Rao²

2019

IJISA

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“…To overcome this limitation in [3,8,12], the SINR improvement of a detected target has been used as a metric. For the estimation of the target SINR we have …”

Section: Performance Metricsmentioning

confidence: 99%

Adaptive Clutter Cancellation Techniques for Passive Radars

Pető¹,

Seller²

2018

Topics in Radar Signal Processing

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In radar systems, the ambiguity function of the applied illuminator signal essentially determines the detection capabilities. Zero Doppler interference (ZDI) or close targets returns can mask weak target reflections from higher distances. This is particularly the case for passive radars where the illuminator signal is not under the control of the radar designer. In recent times, great efforts have been carried out to research and develop efficiently working filter algorithms. These adaptive algorithms aim to cancel the undesired interference components in order to enhance the useful dynamic range. A number of different algorithms are operating in the space and also in the time domain. Spatial algorithms apply beamforming techniques, while temporal algorithms utilize the available reference signal to suppress the interferences. The main goal of this chapter is to present and compare the available spatial and temporal adaptive interference cancellation techniques in terms of filtering efficiency and computationcostonreal-lifedata.

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“…where w t j is the jth coordinate of the temporal coefficient vector and J is the DOFs of the temporal adaptive filter. w t is calculated using the following expression [20]:…”

Section: International Journal Of Antennas and Propagationmentioning

confidence: 99%

“…Other techniques such as the variants of the extensive cancellation algorithm (ECA, ECA-B, and ECA-S) apply different time intervals to the coefficient estimation and filtering [10,14]. Iterative algorithms like the least mean square, normalized least mean square, recursive least squares, and so on update the w t vector from sample to sample [11,16,20]. Time domain filtering is a very effective method for the suppression of the time-delayed replicas of the reference signal; however, it is not able to deal with interferences that are non-…”

Section: International Journal Of Antennas and Propagationmentioning

confidence: 99%

Space-Time Adaptive Cancellation in Passive Radar Systems

Pető

Seller

2018

International Journal of Antennas and Propagation

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A critical issue in the realization of passive radar systems is the effective suppression of the zero Doppler interference (ZDI). The performance of the clutter cancellation relies much on the used algorithms. Several state-of-the-art approaches consist in the independent use of spatial and temporal algorithms for ZDI suppression. In this paper, a novel interference cancellation algorithm is proposed, which jointly exploits the available information from both space and time domains. We call this novel method Space-Time Adaptive Cancellation (STAC), and it differs from previous schemes included among the tools of SpaceTime Adaptive Processing (STAP). The STAC algorithm is able to cancel out the high power direct and multipath signals on all the surveillance antenna channels prior to the beamforming and other space domain processing methods. The proposed preprocessing technique facilitates the detection of targets even in the direction of the illuminator or large static scatterers. Moreover, the interference rejection capability is also enhanced in the space domain. The performance of the presented algorithm is verified through simulations and field measurements. The experiments show improvement over the currently available filtering techniques.

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Time domain filter comparison in passive radar systems

Cited by 4 publications

References 9 publications

Weight Matrix-Based Representation of Sub-Optimum Disturbance Cancellation Filters

Weight Matrix-Based Representation of Sub-Optimum Disturbance Cancellation Filters

Adaptive Clutter Cancellation Techniques for Passive Radars

Space-Time Adaptive Cancellation in Passive Radar Systems

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